Staff Machine Learning Engineer, ML Platform
Braze · New York City
About this role
**Staff Machine Learning Engineer, ML Platform** At Braze, we’re a genuinely approachable, exceptionally kind, and intensely passionate team. We’re growing globally while striving for greater equity and opportunity—inside and outside our organization. We’re seeking a **Staff Machine Learning Engineer** to join our **Predictive and Generative AI (PGAI)** team. The team’s mission is to deliver a truly engaging and personalized customer experience through **ML and AI-enhanced marketing solutions**. These solutions run as production systems at global scale—from distributed pipelines that train models for each customer to high-throughput APIs that serve predictions across multiple regions. You’ll own the platform underneath and help make deploying, operating, and scaling ML at Braze **fast, safe, and efficient**. ### What you’ll do - **Identify and drive transformative initiatives** that change how the team runs ML in production (e.g., replatforming queueing/orchestration, overhauling deployment and cloud identity, retiring infrastructure). - **Build and ship at high velocity**—this is a hands-on delivery role where you carry complex infrastructure initiatives from design through production (e.g., multi-region model serving fleets, pipelines that keep hundreds of customer-specific models healthy, CI and deployment tooling). - **Own the platform’s technical vision and production quality bar**: set direction for how models are trained, deployed, served, and observed; lead incident response for ML systems; and drive reliability and cost improvements at scale. - **Drive initiatives that span teams**, partnering across shared infrastructure, deployment tooling, and data systems. - **Raise engineering quality** through design/code review and production readiness for ML systems, and **mentor** other senior engineers and data scientists. - **Connect technical decisions to customer and business outcomes**, and represent the team’s technical perspective to product and engineering.
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